Defect Detection of Stainless Steel Plates Using Deep Learning Technology

In the era of industry 4.0, factories around the world are developing towards automation and artificial intelligence, in which industrial detection plays an important role. After the cutting process, the surface of a stainless steel plate may produce various defects, such as scratches, chisels, and stains. Due to the characteristic of bright reflections on the surface of a stainless steel plate, the traditional manual comparison detection method is time-consuming, laborious, and prone to different detection results due to the interference of high reflection, resulting in the outflow of defective products. This paper used existing mature deep learning models for object detection, YOLOv3 (You Only Look Once) and SSD (Single Shot MultiBox Detector), which are the base network architectures for the defect detection of stainless steel plates, in order to effectively improve the accuracy of stainless steel plate detection. Through image preprocessing, the relative positions of sample defects are marked to improve data processing before training, in order that a large number of image samples can be quickly and effectively processed for training.